#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
##
## Attaching package: 'vip'
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## vi
#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)
#install.packages("BayesFactor")
library(BayesFactor)
## Loading required package: coda
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## Loading required package: Matrix
## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
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#install.packages('locfit')
library(locfit)
## locfit 1.5-9.8 2023-06-11
#install.packages('ggplot2’)
library(ggplot2)
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library(dplyr)
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#install.packages('networkD3')
library(networkD3)
library(rstanarm)
## Loading required package: Rcpp
## This is rstanarm version 2.26.1
## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores())
library(see)
#install.packages('tidyverse')
library(tidyverse)
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#install.packages('caret')
library(caret)
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library(ISLR)
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library(MCMCpack)
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## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
## ## Support provided by the U.S. National Science Foundation
## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
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library(TDAstats)
library(ks)
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## Recall
#install.packages('googledrive')
library(googledrive)
#install.packages('stringr')
library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
##Add Bayesian tests functions
#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 3000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
#for the moment we implement the sign test. Signedrank will follows
probLeft <- mean (diffVector < rope_min)
probRope <- mean (diffVector > rope_min & diffVector < rope_max)
probRight <- mean (diffVector > rope_max)
results = list ("probLeft"=probLeft, "probRope"=probRope,
"probRight"=probRight)
return (results)
}
##Create function to conduct Bayesian Signed Rank Test
BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 30000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
sampledWeights <- rdirichlet(samples,weights)
winLeft <- vector(length = samples)
winRope <- vector(length = samples)
winRight <- vector(length = samples)
for (rep in 1:samples){
currentWeights <- sampledWeights[rep,]
for (i in 1:length(currentWeights)){
for (j in 1:length(currentWeights)){
product= currentWeights[i] * currentWeights[j]
if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
winRight[rep] <- winRight[rep] + product
}
else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
winRope[rep] <- winRope[rep] + product
}
else {
winLeft[rep] <- winLeft[rep] + product
}
}
}
maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
winRight[rep] <- (winRight[rep]==maxWins)*1/winners
winRope[rep] <- (winRope[rep]==maxWins)*1/winners
winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
}
results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
"winRight"=mean(winRight) )
return (results)
}
#Create function to conduct the Bayesian Correlated t.test
#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.
#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
if (rope_max < rope_min){
stop("rope_max should be larger than rope_min")
}
delta <- mean(diff_a_b)
n <- length(diff_a_b)
df <- n-1
stdX <- sd(diff_a_b)
sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
p.left <- pt((rope_min - delta)/sp, df)
p.rope <- pt((rope_max - delta)/sp, df)-p.left
results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
return (results)
}
set.seed(16974)
###################################################5.60.5 ROPE Comparisons for Dissertation
##Random Forest Results
rf_dataset_av<-c(0.8589, 0.9240, 0.9803)
rf_pca.5.60.5_n1_av<-c(0.2739, 0.5473, 0.9069)
rf_pca.5.60.5_n2_av<-c(0.4662, 0.8208, 0.9438)
rf_pca.5.60.5_n3_av<-c(0.6669, 0.4373, 0.5187)
rf_pca.5.60.5_n4_av<-c(0.8345, 0.3127, 0.1265)
rf_pca.5.60.5_n5_av<-c(0.7890, NA, 0.1004)
rf_kde.5.60.5_n1_av<-c(0.9239, 0.9135, 0.9914)
rf_kde.5.60.5_n2_av<-c(0.9266, 0.8922, 0.9179)
rf_kde.5.60.5_n3_av<-c(0.9167, 0.7078, 0.8473)
rf_kde.5.60.5_n4_av<-c(0.8757, 0.4971, 0.9475)
rf_kde.5.60.5_n5_av<-c(0.8692, 0.4191, 0.9568)
######################## ROPE PCA
diff_rf_pca.5.60.5_n1_av<-rf_dataset_av - rf_pca.5.60.5_n1_av
bsr_diff_rf_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.009366667
##
## $winRight
## [1] 0.9906333
bsr_diff_rf_pca.5.60.5_n1_av_odds.left<-bsr_diff_rf_pca.5.60.5_n1_av $winLeft/bsr_diff_rf_pca.5.60.5_n1_av $winRight
bsr_diff_rf_pca.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_rf_pca.5.60.5_n2_av<-rf_dataset_av - rf_pca.5.60.5_n2_av
bsr_diff_rf_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0087
##
## $winRight
## [1] 0.9913
bsr_diff_rf_pca.5.60.5_n2_av_odds.left<-bsr_diff_rf_pca.5.60.5_n2_av $winLeft/bsr_diff_rf_pca.5.60.5_n2_av $winRight
bsr_diff_rf_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_rf_pca.5.60.5_n3_av<-rf_dataset_av - rf_pca.5.60.5_n3_av
bsr_diff_rf_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008533333
##
## $winRight
## [1] 0.9914667
bsr_diff_rf_pca.5.60.5_n3_av_odds.left<-bsr_diff_rf_pca.5.60.5_n3_av $winLeft/bsr_diff_rf_pca.5.60.5_n3_av $winRight
bsr_diff_rf_pca.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_rf_pca.5.60.5_n4_av<-rf_dataset_av - rf_pca.5.60.5_n4_av
bsr_diff_rf_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_rf_pca.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0097
##
## $winRight
## [1] 0.9903
bsr_diff_rf_pca.5.60.5_n4_av_odds.left<-bsr_diff_rf_pca.5.60.5_n4_av $winLeft/bsr_diff_rf_pca.5.60.5_n4_av $winRight
bsr_diff_rf_pca.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_rf_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_rf_pca.5.60.5_n5_av<-rf_dataset_av - rf_pca.5.60.5_n5_av
#bsr_diff_rf_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_rf_pca.5.60.5_n5_av
#bsr_diff_rf_pca.5.60.5_n5_av_odds.left<-bsr_diff_rf_pca.5.60.5_n5_av $winLeft/bsr_diff_rf_pca.5.60.5_n5_av $winRight
#bsr_diff_rf_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_rf_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_rf_kde.5.60.5_n1_av<-rf_dataset_av - rf_kde.5.60.5_n1_av
bsr_diff_rf_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n1_av
## $winLeft
## [1] 0.6762667
##
## $winRope
## [1] 0.2452667
##
## $winRight
## [1] 0.07846667
bsr_diff_rf_kde.5.60.5_n1_av_odds.left<-bsr_diff_rf_kde.5.60.5_n1_av $winLeft/bsr_diff_rf_kde.5.60.5_n1_av $winRight
bsr_diff_rf_kde.5.60.5_n1_av_odds.left
## [1] 8.618522
plot(rope(diff_rf_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n2_av<-rf_dataset_av - rf_kde.5.60.5_n2_av
bsr_diff_rf_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n2_av
## $winLeft
## [1] 0.3331
##
## $winRope
## [1] 0.0492
##
## $winRight
## [1] 0.6177
bsr_diff_rf_kde.5.60.5_n2_av_odds.left<-bsr_diff_rf_kde.5.60.5_n2_av $winLeft/bsr_diff_rf_kde.5.60.5_n2_av $winRight
bsr_diff_rf_kde.5.60.5_n2_av_odds.left
## [1] 0.5392585
plot(rope(diff_rf_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n3_av<-rf_dataset_av - rf_kde.5.60.5_n3_av
bsr_diff_rf_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n3_av
## $winLeft
## [1] 0.1061
##
## $winRope
## [1] 0.01453333
##
## $winRight
## [1] 0.8793667
bsr_diff_rf_kde.5.60.5_n3_av_odds.left<-bsr_diff_rf_kde.5.60.5_n3_av $winLeft/bsr_diff_rf_kde.5.60.5_n3_av $winRight
bsr_diff_rf_kde.5.60.5_n3_av_odds.left
## [1] 0.120655
plot(rope(diff_rf_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n4_av<-rf_dataset_av - rf_kde.5.60.5_n4_av
bsr_diff_rf_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n4_av
## $winLeft
## [1] 0.08276667
##
## $winRope
## [1] 0.1408333
##
## $winRight
## [1] 0.7764
bsr_diff_rf_kde.5.60.5_n4_av_odds.left<-bsr_diff_rf_kde.5.60.5_n4_av $winLeft/bsr_diff_rf_kde.5.60.5_n4_av $winRight
bsr_diff_rf_kde.5.60.5_n4_av_odds.left
## [1] 0.1066031
plot(rope(diff_rf_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_rf_kde.5.60.5_n5_av<-rf_dataset_av - rf_kde.5.60.5_n5_av
bsr_diff_rf_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_rf_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_rf_kde.5.60.5_n5_av
## $winLeft
## [1] 0.0806
##
## $winRope
## [1] 0.1364667
##
## $winRight
## [1] 0.7829333
bsr_diff_rf_kde.5.60.5_n5_av_odds.left<-bsr_diff_rf_kde.5.60.5_n5_av $winLeft/bsr_diff_rf_kde.5.60.5_n5_av $winRight
bsr_diff_rf_kde.5.60.5_n5_av_odds.left
## [1] 0.1029462
plot(rope(diff_rf_kde.5.60.5_n5_av,c(-0.01,0.01)))
################################ Support Vector Machine
##Support Vector Machine Results
svm_dataset_av<-c(0.8270, 0.9275, 0.9778)
svm_pca.5.60.5_n1_av<-c(0.3774, 0.5336, 0.9015)
svm_pca.5.60.5_n2_av<-c(0.3774, 0.8576, 0.9249)
svm_pca.5.60.5_n3_av<-c(0.7659, 0.4341, 0.4804)
svm_pca.5.60.5_n4_av<-c(0.7640, 0.3370, 0.1222)
svm_pca.5.60.5_n5_av<-c(0.7592, NA, 0.0985)
svm_kde.5.60.5_n1_av<-c(0.8604, 0.9047, 0.9030)
svm_kde.5.60.5_n2_av<-c(0.8657, 0.9470, 0.9082)
svm_kde.5.60.5_n3_av<-c(0.8504, 0.6194, 0.9031)
svm_kde.5.60.5_n4_av<-c(0.8231, 0.3931, 0.9026)
svm_kde.5.60.5_n5_av<-c(0.8529, 0.3005, 0.9026)
######################## ROPE PCA
diff_svm_pca.5.60.5_n1_av<-svm_dataset_av - svm_pca.5.60.5_n1_av
bsr_diff_svm_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008133333
##
## $winRight
## [1] 0.9918667
bsr_diff_svm_pca.5.60.5_n1_av_odds.left<-bsr_diff_svm_pca.5.60.5_n1_av$winLeft/bsr_diff_svm_pca.5.60.5_n1_av $winRight
bsr_diff_svm_pca.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_svm_pca.5.60.5_n2_av<-svm_dataset_av - svm_pca.5.60.5_n2_av
bsr_diff_svm_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008233333
##
## $winRight
## [1] 0.9917667
bsr_diff_svm_pca.5.60.5_n2_av_odds.left<-bsr_diff_svm_pca.5.60.5_n2_av$winLeft/bsr_diff_svm_pca.5.60.5_n1_av $winRight
bsr_diff_svm_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_svm_pca.5.60.5_n3_av<-svm_dataset_av - svm_pca.5.60.5_n3_av
bsr_diff_svm_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008933333
##
## $winRight
## [1] 0.9910667
bsr_diff_svm_pca.5.60.5_n3_av_odds.left<-bsr_diff_svm_pca.5.60.5_n3_av$winLeft/bsr_diff_svm_pca.5.60.5_n3_av $winRight
bsr_diff_svm_pca.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_svm_pca.5.60.5_n4_av<-svm_dataset_av - svm_pca.5.60.5_n4_av
bsr_diff_svm_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_svm_pca.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0093
##
## $winRight
## [1] 0.9907
bsr_diff_svm_pca.5.60.5_n4_av_odds.left<-bsr_diff_svm_pca.5.60.5_n4_av$winLeft/bsr_diff_svm_pca.5.60.5_n4_av $winRight
bsr_diff_svm_pca.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_svm_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_svm_pca.5.60.5_n5_av<-svm_dataset_av - svm_pca.5.60.5_n5_av
#bsr_diff_svm_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_svm_pca.5.60.5_n5_av
#bsr_diff_svm_pca.5.60.5_n5_av_odds.left<-bsr_diff_svm_pca.5.60.5_n5_av$winLeft/bsr_diff_svm_pca.5.60.5_n5_av $winRight
#bsr_diff_svm_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_svm_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_svm_kde.5.60.5_n1_av<-svm_dataset_av - svm_kde.5.60.5_n1_av
bsr_diff_svm_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n1_av
## $winLeft
## [1] 0.1544667
##
## $winRope
## [1] 0.04713333
##
## $winRight
## [1] 0.7984
bsr_diff_svm_kde.5.60.5_n1_av_odds.left<-bsr_diff_svm_kde.5.60.5_n1_av $winLeft/bsr_diff_svm_kde.5.60.5_n1_av $winRight
bsr_diff_svm_kde.5.60.5_n1_av_odds.left
## [1] 0.1934703
plot(rope(diff_svm_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n2_av<-svm_dataset_av - svm_kde.5.60.5_n2_av
bsr_diff_svm_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n2_av
## $winLeft
## [1] 0.4712667
##
## $winRope
## [1] 0.0594
##
## $winRight
## [1] 0.4693333
bsr_diff_svm_kde.5.60.5_n2_av_odds.left<-bsr_diff_svm_kde.5.60.5_n2_av $winLeft/bsr_diff_svm_kde.5.60.5_n2_av $winRight
bsr_diff_svm_kde.5.60.5_n2_av_odds.left
## [1] 1.004119
plot(rope(diff_svm_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n3_av<-svm_dataset_av - svm_kde.5.60.5_n3_av
bsr_diff_svm_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n3_av
## $winLeft
## [1] 0.1089
##
## $winRope
## [1] 0.01576667
##
## $winRight
## [1] 0.8753333
bsr_diff_svm_kde.5.60.5_n3_av_odds.left<-bsr_diff_svm_kde.5.60.5_n3_av $winLeft/bsr_diff_svm_kde.5.60.5_n3_av $winRight
bsr_diff_svm_kde.5.60.5_n3_av_odds.left
## [1] 0.1244097
plot(rope(diff_svm_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n4_av<-svm_dataset_av - svm_kde.5.60.5_n4_av
bsr_diff_svm_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1431333
##
## $winRight
## [1] 0.8568667
bsr_diff_svm_kde.5.60.5_n4_av_odds.left<-bsr_diff_svm_kde.5.60.5_n4_av $winLeft/bsr_diff_svm_kde.5.60.5_n4_av $winRight
bsr_diff_svm_kde.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_svm_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_svm_kde.5.60.5_n5_av<-svm_dataset_av - svm_kde.5.60.5_n5_av
bsr_diff_svm_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_svm_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_svm_kde.5.60.5_n5_av
## $winLeft
## [1] 0.1100667
##
## $winRope
## [1] 0.01543333
##
## $winRight
## [1] 0.8745
bsr_diff_svm_kde.5.60.5_n5_av_odds.left<-bsr_diff_svm_kde.5.60.5_n5_av $winLeft/bsr_diff_svm_kde.5.60.5_n5_av $winRight
bsr_diff_svm_kde.5.60.5_n5_av_odds.left
## [1] 0.1258624
plot(rope(diff_svm_kde.5.60.5_n5_av,c(-0.01,0.01)))
######################### Neural Network
##Neural Network Results
nn1_dataset_av<-c(0.8051, 0.3779, 0.9812)
nn1_pca.5.60.5_n1_av<-c(0.2408, 0.3995, 0.9015)
nn1_pca.5.60.5_n2_av<-c(0.4930, 0.5468, 0.9303)
nn1_pca.5.60.5_n3_av<-c(0.7439, 0.3311, 0.9747)
nn1_pca.5.60.5_n4_av<-c(0.7592, 0.2806, 0.1190)
nn1_pca.5.60.5_n5_av<-c(0.7790, 0.0382, 0.0985)
nn1_kde.5.60.5_n1_av<-c(0.7987, 0.2059, 0.9808)
nn1_kde.5.60.5_n2_av<-c(0.8003, 0.4429, 0.9778)
nn1_kde.5.60.5_n3_av<-c(0.8010, 0.5488, 0.9775)
nn1_kde.5.60.5_n4_av<-c(0.8007, 0.3961, 0.9745)
nn1_kde.5.60.5_n5_av<-c(0.7483, 0.3801, 0.9015)
######################## ROPE PCA
diff_nn1_pca.5.60.5_n1_av<-nn1_dataset_av - nn1_pca.5.60.5_n1_av
bsr_diff_nn1_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n1_av
## $winLeft
## [1] 0.1099667
##
## $winRope
## [1] 0.0144
##
## $winRight
## [1] 0.8756333
bsr_diff_nn1_pca.5.60.5_n1_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n1_av$winLeft/bsr_diff_nn1_pca.5.60.5_n1_av $winRight
bsr_diff_nn1_pca.5.60.5_n1_av_odds.left
## [1] 0.1255853
plot(rope(diff_nn1_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n2_av<-nn1_dataset_av - nn1_pca.5.60.5_n2_av
bsr_diff_nn1_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n2_av
## $winLeft
## [1] 0.26
##
## $winRope
## [1] 0.01703333
##
## $winRight
## [1] 0.7229667
bsr_diff_nn1_pca.5.60.5_n2_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n2_av$winLeft/bsr_diff_nn1_pca.5.60.5_n2_av $winRight
bsr_diff_nn1_pca.5.60.5_n2_av_odds.left
## [1] 0.3596293
plot(rope(diff_nn1_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n3_av<-nn1_dataset_av - nn1_pca.5.60.5_n3_av
bsr_diff_nn1_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1406333
##
## $winRight
## [1] 0.8593667
bsr_diff_nn1_pca.5.60.5_n3_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n3_av$winLeft/bsr_diff_nn1_pca.5.60.5_n3_av $winRight
bsr_diff_nn1_pca.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n4_av<-nn1_dataset_av - nn1_pca.5.60.5_n4_av
bsr_diff_nn1_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0096
##
## $winRight
## [1] 0.9904
bsr_diff_nn1_pca.5.60.5_n4_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n4_av$winLeft/bsr_diff_nn1_pca.5.60.5_n4_av $winRight
bsr_diff_nn1_pca.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.60.5_n4_av,c(-0.01,0.01)))
diff_nn1_pca.5.60.5_n5_av<-nn1_dataset_av - nn1_pca.5.60.5_n5_av
bsr_diff_nn1_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_pca.5.60.5_n5_av),-0.01,0.01)
bsr_diff_nn1_pca.5.60.5_n5_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0087
##
## $winRight
## [1] 0.9913
bsr_diff_nn1_pca.5.60.5_n5_av_odds.left<-bsr_diff_nn1_pca.5.60.5_n5_av$winLeft/bsr_diff_nn1_pca.5.60.5_n5_av $winRight
bsr_diff_nn1_pca.5.60.5_n5_av_odds.left
## [1] 0
plot(rope(diff_nn1_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_nn1_kde.5.60.5_n1_av<-nn1_dataset_av - nn1_kde.5.60.5_n1_av
bsr_diff_nn1_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5751667
##
## $winRight
## [1] 0.4248333
bsr_diff_nn1_kde.5.60.5_n1_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n1_av $winLeft/bsr_diff_nn1_kde.5.60.5_n1_av $winRight
bsr_diff_nn1_kde.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n2_av<-nn1_dataset_av - nn1_kde.5.60.5_n2_av
bsr_diff_nn1_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n2_av
## $winLeft
## [1] 0.4201667
##
## $winRope
## [1] 0.5798333
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n2_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n2_av $winLeft/bsr_diff_nn1_kde.5.60.5_n2_av $winRight
bsr_diff_nn1_kde.5.60.5_n2_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n3_av<-nn1_dataset_av - nn1_kde.5.60.5_n3_av
bsr_diff_nn1_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n3_av
## $winLeft
## [1] 0.4226333
##
## $winRope
## [1] 0.5773667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n3_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n3_av $winLeft/bsr_diff_nn1_kde.5.60.5_n3_av $winRight
bsr_diff_nn1_kde.5.60.5_n3_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n4_av<-nn1_dataset_av - nn1_kde.5.60.5_n4_av
bsr_diff_nn1_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n4_av
## $winLeft
## [1] 0.04593333
##
## $winRope
## [1] 0.9540667
##
## $winRight
## [1] 0
bsr_diff_nn1_kde.5.60.5_n4_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n4_av $winLeft/bsr_diff_nn1_kde.5.60.5_n4_av $winRight
bsr_diff_nn1_kde.5.60.5_n4_av_odds.left
## [1] Inf
plot(rope(diff_nn1_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_nn1_kde.5.60.5_n5_av<-nn1_dataset_av - nn1_kde.5.60.5_n5_av
bsr_diff_nn1_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nn1_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_nn1_kde.5.60.5_n5_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1474333
##
## $winRight
## [1] 0.8525667
bsr_diff_nn1_kde.5.60.5_n5_av_odds.left<-bsr_diff_nn1_kde.5.60.5_n5_av $winLeft/bsr_diff_nn1_kde.5.60.5_n5_av $winRight
bsr_diff_nn1_kde.5.60.5_n5_av_odds.left
## [1] 0
plot(rope(diff_nn1_kde.5.60.5_n5_av,c(-0.01,0.01)))
################################ Logistic Regression
##Logistic Regression Results
lr_dataset_av<-c(0.8533, 0.9245, 0.9808)
lr_pca.5.60.5_n1_av<-c(0.2436, 0.5740, 0.9033)
lr_pca.5.60.5_n2_av<-c(0.4611, 0.8836, 0.9724)
lr_pca.5.60.5_n3_av<-c(0.7656, 0.6105, 0.9704)
lr_pca.5.60.5_n4_av<-c(0.7829, 0.3431, 0.3524)
lr_pca.5.60.5_n5_av<-c(0.7629, NA, 0.0982)
lr_kde.5.60.5_n1_av<-c(0.8519, 0.8968, 0.9795)
lr_kde.5.60.5_n2_av<-c(0.8458, 0.8787, 0.9791)
lr_kde.5.60.5_n3_av<-c(0.8382, 0.7556, 0.9743)
lr_kde.5.60.5_n4_av<-c(0.7817, 0.6623, 0.9685)
lr_kde.5.60.5_n5_av<-c(0.8199, 0.5659, 0.9629)
######################## ROPE PCA
diff_lr_pca.5.60.5_n1_av<-lr_dataset_av - lr_pca.5.60.5_n1_av
bsr_diff_lr_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008133333
##
## $winRight
## [1] 0.9918667
bsr_diff_lr_pca.5.60.5_n1_av_odds.left<-bsr_diff_lr_pca.5.60.5_n1_av$winLeft/bsr_diff_lr_pca.5.60.5_n1_av $winRight
bsr_diff_lr_pca.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_lr_pca.5.60.5_n2_av<-lr_dataset_av - lr_pca.5.60.5_n2_av
bsr_diff_lr_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1461333
##
## $winRight
## [1] 0.8538667
bsr_diff_lr_pca.5.60.5_n2_av_odds.left<-bsr_diff_lr_pca.5.60.5_n2_av$winLeft/bsr_diff_lr_pca.5.60.5_n2_av $winRight
bsr_diff_lr_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.60.5_n2_av,c(-0.01,0.01)))
diff_lr_pca.5.60.5_n3_av<-lr_dataset_av - lr_pca.5.60.5_n3_av
bsr_diff_lr_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n3_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.03703333
##
## $winRight
## [1] 0.9629667
bsr_diff_lr_pca.5.60.5_n3_av_odds.left<-bsr_diff_lr_pca.5.60.5_n3_av$winLeft/bsr_diff_lr_pca.5.60.5_n3_av $winRight
bsr_diff_lr_pca.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_lr_pca.5.60.5_n4_av<-lr_dataset_av - lr_pca.5.60.5_n4_av
bsr_diff_lr_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_lr_pca.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008833333
##
## $winRight
## [1] 0.9911667
bsr_diff_lr_pca.5.60.5_n4_av_odds.left<-bsr_diff_lr_pca.5.60.5_n4_av$winLeft/bsr_diff_lr_pca.5.60.5_n4_av $winRight
bsr_diff_lr_pca.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_lr_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_lr_pca.5.60.5_n5_av<-lr_dataset_av - lr_pca.5.60.5_n5_av
#bsr_diff_lr_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_lr_pca.5.60.5_n5_av
#bsr_diff_lr_pca.5.60.5_n5_av_odds.left<-bsr_diff_lr_pca.5.60.5_n5_av$winLeft/bsr_diff_lr_pca.5.60.5_n5_av $winRight
#bsr_diff_lr_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_lr_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_lr_kde.5.60.5_n1_av<-lr_dataset_av - lr_kde.5.60.5_n1_av
bsr_diff_lr_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5761333
##
## $winRight
## [1] 0.4238667
bsr_diff_lr_kde.5.60.5_n1_av_odds.left<-bsr_diff_lr_kde.5.60.5_n1_av $winLeft/bsr_diff_lr_kde.5.60.5_n1_av $winRight
bsr_diff_lr_kde.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n2_av<-lr_dataset_av - lr_kde.5.60.5_n2_av
bsr_diff_lr_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5850333
##
## $winRight
## [1] 0.4149667
bsr_diff_lr_kde.5.60.5_n2_av_odds.left<-bsr_diff_lr_kde.5.60.5_n2_av $winLeft/bsr_diff_lr_kde.5.60.5_n2_av $winRight
bsr_diff_lr_kde.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n2_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n3_av<-lr_dataset_av - lr_kde.5.60.5_n3_av
bsr_diff_lr_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n3_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n3_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.2133667
##
## $winRight
## [1] 0.7866333
bsr_diff_lr_kde.5.60.5_n3_av_odds.left<-bsr_diff_lr_kde.5.60.5_n3_av $winLeft/bsr_diff_lr_kde.5.60.5_n3_av $winRight
bsr_diff_lr_kde.5.60.5_n3_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n4_av<-lr_dataset_av - lr_kde.5.60.5_n4_av
bsr_diff_lr_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.03673333
##
## $winRight
## [1] 0.9632667
bsr_diff_lr_kde.5.60.5_n4_av_odds.left<-bsr_diff_lr_kde.5.60.5_n4_av $winLeft/bsr_diff_lr_kde.5.60.5_n4_av $winRight
bsr_diff_lr_kde.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n4_av,c(-0.01,0.01)))
diff_lr_kde.5.60.5_n5_av<-lr_dataset_av - lr_kde.5.60.5_n5_av
bsr_diff_lr_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_lr_kde.5.60.5_n5_av),-0.01,0.01)
bsr_diff_lr_kde.5.60.5_n5_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.03843333
##
## $winRight
## [1] 0.9615667
bsr_diff_lr_kde.5.60.5_n5_av_odds.left<-bsr_diff_lr_kde.5.60.5_n5_av $winLeft/bsr_diff_lr_kde.5.60.5_n5_av $winRight
bsr_diff_lr_kde.5.60.5_n5_av_odds.left
## [1] 0
plot(rope(diff_lr_kde.5.60.5_n5_av,c(-0.01,0.01)))
#################################################### Naive Bayes
##Naive Bayes Results
nb_dataset_av<-c(0.7728, 0.8944, 0.9667)
nb_pca.5.60.5_n1_av<-c(0.2408, 0.5980, 0.9290)
nb_pca.5.60.5_n2_av<-c(0.7634, 0.8417, 0.9555)
#nb_pca.5.60.5_n3_av<-c0.7743, NA, 0.9637)
nb_pca.5.60.5_n4_av<-c(0.7592, 0.3473, 0.7500)
#nb_pca.5.60.5_n5_av<-c(0.7592, NA, NA)
nb_kde.5.60.5_n1_av<-c(0.7631, 0.8485, 0.9711)
nb_kde.5.60.5_n2_av<-c(0.7634, 0.8417, 0.9555)
#nb_kde.5.60.5_n3_av<-c(0.7743, NA, 0.9637)
nb_kde.5.60.5_n4_av<-c(0.7592, 0.3473, 0.7156)
#nb_kde.5.60.5_n5_av<-c(0.7592, NA, NA)
######################## ROPE PCA
diff_nb_pca.5.60.5_n1_av<-nb_dataset_av - nb_pca.5.60.5_n1_av
bsr_diff_nb_pca.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nb_pca.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0085
##
## $winRight
## [1] 0.9915
bsr_diff_nb_pca.5.60.5_n1_av_odds.left<-bsr_diff_nb_pca.5.60.5_n1_av$winLeft/bsr_diff_nb_pca.5.60.5_n1_av $winRight
bsr_diff_nb_pca.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nb_pca.5.60.5_n1_av,c(-0.01,0.01)))
diff_nb_pca.5.60.5_n2_av<-nb_dataset_av - nb_pca.5.60.5_n2_av
bsr_diff_nb_pca.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nb_pca.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.2208667
##
## $winRight
## [1] 0.7791333
bsr_diff_nb_pca.5.60.5_n2_av_odds.left<-bsr_diff_nb_pca.5.60.5_n2_av$winLeft/bsr_diff_nb_pca.5.60.5_n2_av $winRight
bsr_diff_nb_pca.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nb_pca.5.60.5_n2_av,c(-0.01,0.01)))
#diff_nb_pca.5.60.5_n3_av<-nb_dataset_av - nb_pca.5.60.5_n3_av
#bsr_diff_nb_pca.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n3_av),-0.01,0.01)
#bsr_diff_nb_pca.5.60.5_n3_av
#bsr_diff_nb_pca.5.60.5_n3_av_odds.left<-bsr_diff_nb_pca.5.60.5_n3_av$winLeft/bsr_diff_nb_pca.5.60.5_n3_av $winRight
#bsr_diff_nb_pca.5.60.5_n3_av_odds.left
#plot(rope(diff_nb_pca.5.60.5_n3_av,c(-0.01,0.01)))
diff_nb_pca.5.60.5_n4_av<-nb_dataset_av - nb_pca.5.60.5_n4_av
bsr_diff_nb_pca.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nb_pca.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.03686667
##
## $winRight
## [1] 0.9631333
bsr_diff_nb_pca.5.60.5_n4_av_odds.left<-bsr_diff_nb_pca.5.60.5_n4_av$winLeft/bsr_diff_nb_pca.5.60.5_n4_av$winRight
bsr_diff_nb_pca.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nb_pca.5.60.5_n4_av,c(-0.01,0.01)))
#diff_nb_pca.5.60.5_n5_av<-nb_dataset_av - nb_pca.5.60.5_n5_av
#bsr_diff_nb_pca.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_pca.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_nb_pca.5.60.5_n5_av
#bsr_diff_nb_pca.5.60.5_n5_av_odds.left<-bsr_diff_nb_pca.5.60.5_n5_av$winLeft/bsr_diff_nb_pca.5.60.5_n5_av $winRight
#bsr_diff_nb_pca.5.60.5_n5_av_odds.left
#plot(rope(diff_nb_pca.5.60.5_n5_av,c(-0.01,0.01)))
########################## ROPE KDE
diff_nb_kde.5.60.5_n1_av<-nb_dataset_av - nb_kde.5.60.5_n1_av
bsr_diff_nb_kde.5.60.5_n1_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n1_av),-0.01,0.01)
bsr_diff_nb_kde.5.60.5_n1_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.5784333
##
## $winRight
## [1] 0.4215667
bsr_diff_nb_kde.5.60.5_n1_av_odds.left<-bsr_diff_nb_kde.5.60.5_n1_av$winLeft/bsr_diff_nb_kde.5.60.5_n1_av$winRight
bsr_diff_nb_kde.5.60.5_n1_av_odds.left
## [1] 0
plot(rope(diff_nb_kde.5.60.5_n1_av,c(-0.01,0.01)))
diff_nb_kde.5.60.5_n2_av<-nb_dataset_av - nb_kde.5.60.5_n2_av
bsr_diff_nb_kde.5.60.5_n2_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n2_av),-0.01,0.01)
bsr_diff_nb_kde.5.60.5_n2_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.2089
##
## $winRight
## [1] 0.7911
bsr_diff_nb_kde.5.60.5_n2_av_odds.left<-bsr_diff_nb_kde.5.60.5_n2_av$winLeft/bsr_diff_nb_kde.5.60.5_n2_av$winRight
bsr_diff_nb_kde.5.60.5_n2_av_odds.left
## [1] 0
plot(rope(diff_nb_kde.5.60.5_n2_av,c(-0.01,0.01)))
#diff_nb_kde.5.60.5_n3_av<-nb_dataset_av - nb_kde.5.60.5_n3_av
#bsr_diff_nb_kde.5.60.5_n3_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n3_av),-0.01,0.01)
#bsr_diff_nb_kde.5.60.5_n3_av
#bsr_diff_nb_kde.5.60.5_n3_av_odds.left<-bsr_diff_nb_kde.5.60.5_n3_av $winLeft/bsr_diff_nb_kde.5.60.5_n3_av $winRight
#bsr_diff_nb_kde.5.60.5_n3_av_odds.left
#plot(rope(diff_nb_kde.5.60.5_n3_av,c(-0.01,0.01)))
diff_nb_kde.5.60.5_n4_av<-nb_dataset_av - nb_kde.5.60.5_n4_av
bsr_diff_nb_kde.5.60.5_n4_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n4_av),-0.01,0.01)
bsr_diff_nb_kde.5.60.5_n4_av
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0361
##
## $winRight
## [1] 0.9639
bsr_diff_nb_kde.5.60.5_n4_av_odds.left<-bsr_diff_nb_kde.5.60.5_n4_av $winLeft/bsr_diff_nb_kde.5.60.5_n4_av $winRight
bsr_diff_nb_kde.5.60.5_n4_av_odds.left
## [1] 0
plot(rope(diff_nb_kde.5.60.5_n4_av,c(-0.01,0.01)))
#diff_nb_kde.5.60.5_n5_av<-nb_dataset_av - nb_kde.5.60.5_n5_av
#bsr_diff_nb_kde.5.60.5_n5_av<-BayesianSignedRank(as.matrix(diff_nb_kde.5.60.5_n5_av),-0.01,0.01)
#bsr_diff_nb_kde.5.60.5_n5_av
#bsr_diff_nb_kde.5.60.5_n5_av_odds.left<-bsr_diff_nb_kde.5.60.5_n5_av $winLeft/bsr_diff_nb_kde.5.60.5_n5_av $winRight
#bsr_diff_nb_kde.5.60.5_n5_av_odds.left
#plot(rope(diff_nb_kde.5.60.5_n5_av,c(-0.01,0.01)))